






"""simple docstring"""
import numpy as np

from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch

from .test_feature_extraction_common import FeatureExtractionSavingTestMixin



class        UpperCamelCase__ (				a_):







    """simple docstring"""
    # to overwrite at feature extractactor specific tests
    __UpperCAmelCase           = None
    __UpperCAmelCase           = None







    @property
    def 				a__			(		self :	int ):





           '''simple docstring'''

           return self.feat_extract_tester.prepare_feat_extract_dict()







    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    self.feature_extraction_class(**self.feat_extract_dict )
           self.assertTrue(hasattr(UpperCamelCase_				,    'feature_size' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'sampling_rate' ) )
           self.assertTrue(hasattr(UpperCamelCase_				,    'padding_value' ) )







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__      				=    self.feat_extract_tester.prepare_inputs_for_common()
           __magic_name__      				=    self.feature_extraction_class(**self.feat_extract_dict )
           __magic_name__      				=    feat_extract.model_input_names[0]

           __magic_name__      				=    BatchFeature({input_name: speech_inputs} )

           self.assertTrue(all(len(UpperCamelCase_ ) == len(UpperCamelCase_ ) for x, y in zip(UpperCamelCase_				,    processed_features[input_name] ) ) )

           __magic_name__      				=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_ )
           __magic_name__      				=    BatchFeature({input_name: speech_inputs}				,    tensor_type='np' )

           __magic_name__      				=    processed_features[input_name]

           if len(batch_features_input.shape ) < 3:
                  __magic_name__      				=    batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )







    @require_torch
    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_ )
           __magic_name__      				=    self.feature_extraction_class(**self.feat_extract_dict )
           __magic_name__      				=    feat_extract.model_input_names[0]

           __magic_name__      				=    BatchFeature({input_name: speech_inputs}				,    tensor_type='pt' )

           __magic_name__      				=    processed_features[input_name]

           if len(batch_features_input.shape ) < 3:
                  __magic_name__      				=    batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )







    @require_tf
    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    self.feat_extract_tester.prepare_inputs_for_common(equal_length=UpperCamelCase_ )
           __magic_name__      				=    self.feature_extraction_class(**self.feat_extract_dict )
           __magic_name__      				=    feat_extract.model_input_names[0]

           __magic_name__      				=    BatchFeature({input_name: speech_inputs}				,    tensor_type='tf' )

           __magic_name__      				=    processed_features[input_name]

           if len(batch_features_input.shape ) < 3:
                  __magic_name__      				=    batch_features_input[:, :, None]

           self.assertTrue(
               batch_features_input.shape
               == (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )







    def 				a__			(		self :	int				,    UpperCamelCase_ :	Tuple=False ):





           '''simple docstring'''

           def _inputs_have_equal_length(UpperCamelCase_ :	str ):
                  __magic_name__      				=    len(input[0] )
                  for input_slice in input[1:]:
                         if len(UpperCamelCase_ ) != length:
                                return False
                  return True

           def _inputs_are_equal(UpperCamelCase_ :	Dict				,    UpperCamelCase_ :	Tuple ):
                  if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
                         return False

                  for input_slice_a, input_slice_a in zip(UpperCamelCase_				,    UpperCamelCase_ ):
                         if not np.allclose(np.asarray(UpperCamelCase_ )				,    np.asarray(UpperCamelCase_ )				,    atol=1e-3 ):
                                return False
                  return True

           __magic_name__      				=    self.feature_extraction_class(**self.feat_extract_dict )
           __magic_name__      				=    self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase_ )
           __magic_name__      				=    feat_extract.model_input_names[0]

           __magic_name__      				=    BatchFeature({input_name: speech_inputs} )

           __magic_name__      				=    self.feat_extract_tester.seq_length_diff
           __magic_name__      				=    self.feat_extract_tester.max_seq_length + pad_diff
           __magic_name__      				=    self.feat_extract_tester.min_seq_length
           __magic_name__      				=    self.feat_extract_tester.batch_size
           __magic_name__      				=    self.feat_extract_tester.feature_size

           # test padding for List[int] + numpy
           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding=UpperCamelCase_ )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding='longest' )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding='max_length'				,    max_length=len(speech_inputs[-1] ) )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding='longest'				,    return_tensors='np' )
           __magic_name__      				=    input_a[input_name]

           # max_length parameter has to be provided when setting `padding="max_length"`
           with self.assertRaises(UpperCamelCase_ ):
                  feat_extract.pad(UpperCamelCase_				,    padding='max_length' )[input_name]

           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    max_length=UpperCamelCase_				,    return_tensors='np' )
           __magic_name__      				=    input_a[input_name]

           self.assertFalse(_inputs_have_equal_length(UpperCamelCase_ ) )
           self.assertTrue(_inputs_have_equal_length(UpperCamelCase_ ) )
           self.assertTrue(_inputs_have_equal_length(UpperCamelCase_ ) )
           self.assertTrue(_inputs_are_equal(UpperCamelCase_				,    UpperCamelCase_ ) )
           self.assertTrue(len(input_a[0] ) == pad_min_length )
           self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
           self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
           self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )

           if feature_size > 1:
                  self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )

           # test padding for `pad_to_multiple_of` for List[int] + numpy
           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    pad_to_multiple_of=1_0 )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding='longest'				,    pad_to_multiple_of=1_0 )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    pad_to_multiple_of=1_0				,    max_length=UpperCamelCase_ )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    pad_to_multiple_of=1_0				,    max_length=UpperCamelCase_				,    return_tensors='np'				,    )
           __magic_name__      				=    input_a[input_name]

           self.assertTrue(all(len(UpperCamelCase_ ) % 1_0 == 0 for x in input_a ) )
           self.assertTrue(_inputs_are_equal(UpperCamelCase_				,    UpperCamelCase_ ) )

           __magic_name__      				=    pad_max_length if pad_max_length % 1_0 == 0 else (pad_max_length // 1_0 + 1) * 1_0
           self.assertTrue(all(len(UpperCamelCase_ ) == expected_mult_pad_length for x in input_a ) )
           self.assertEqual(input_a.shape[:2]				,    (batch_size, expected_mult_pad_length) )

           if feature_size > 1:
                  self.assertTrue(input_a.shape[2] == feature_size )

           # Check padding value is correct
           __magic_name__      				=    (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
           self.assertTrue(
               abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
               < 1e-3 )
           self.assertTrue(
               abs(
                   np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
                   - padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
               < 1e-3 )
           self.assertTrue(
               abs(
                   np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
                   - padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
               < 1e-3 )
           self.assertTrue(
               abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
           self.assertTrue(
               abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
               < 1e-3 )







    def 				a__			(		self :	Union[str, Any]				,    UpperCamelCase_ :	Dict=False ):





           '''simple docstring'''

           def _inputs_have_equal_length(UpperCamelCase_ :	List[str] ):
                  __magic_name__      				=    len(input[0] )
                  for input_slice in input[1:]:
                         if len(UpperCamelCase_ ) != length:
                                return False
                  return True

           def _inputs_are_equal(UpperCamelCase_ :	Union[str, Any]				,    UpperCamelCase_ :	int ):
                  if len(UpperCamelCase_ ) != len(UpperCamelCase_ ):
                         return False

                  for input_slice_a, input_slice_a in zip(UpperCamelCase_				,    UpperCamelCase_ ):
                         if not np.allclose(np.asarray(UpperCamelCase_ )				,    np.asarray(UpperCamelCase_ )				,    atol=1e-3 ):
                                return False
                  return True

           __magic_name__      				=    self.feature_extraction_class(**self.feat_extract_dict )
           __magic_name__      				=    self.feat_extract_tester.prepare_inputs_for_common(numpify=UpperCamelCase_ )
           __magic_name__      				=    feat_extract.model_input_names[0]

           __magic_name__      				=    BatchFeature({input_name: speech_inputs} )

           # truncate to smallest
           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    max_length=len(speech_inputs[0] )				,    truncation=UpperCamelCase_ )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding='max_length'				,    max_length=len(speech_inputs[0] ) )
           __magic_name__      				=    input_a[input_name]

           self.assertTrue(_inputs_have_equal_length(UpperCamelCase_ ) )
           self.assertFalse(_inputs_have_equal_length(UpperCamelCase_ ) )

           # truncate to smallest with np
           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    max_length=len(speech_inputs[0] )				,    return_tensors='np'				,    truncation=UpperCamelCase_				,    )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    max_length=len(speech_inputs[0] )				,    return_tensors='np' )
           __magic_name__      				=    input_a[input_name]

           self.assertTrue(_inputs_have_equal_length(UpperCamelCase_ ) )
           self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )

           # since truncation forces padding to be smaller than longest input
           # function can't return `np.ndarray`, but has to return list
           self.assertFalse(_inputs_have_equal_length(UpperCamelCase_ ) )

           # truncate to middle
           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    max_length=len(speech_inputs[1] )				,    truncation=UpperCamelCase_				,    return_tensors='np'				,    )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    max_length=len(speech_inputs[1] )				,    truncation=UpperCamelCase_ )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    max_length=len(speech_inputs[1] )				,    return_tensors='np' )
           __magic_name__      				=    input_a[input_name]

           self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
           self.assertTrue(_inputs_have_equal_length(UpperCamelCase_ ) )
           self.assertTrue(_inputs_have_equal_length(UpperCamelCase_ ) )
           self.assertTrue(_inputs_are_equal(UpperCamelCase_				,    UpperCamelCase_ ) )

           # since truncation forces padding to be smaller than longest input
           # function can't return `np.ndarray`, but has to return list
           self.assertFalse(_inputs_have_equal_length(UpperCamelCase_ ) )
           self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )

           # padding has to be max_length when setting `truncation=True`
           with self.assertRaises(UpperCamelCase_ ):
                  feat_extract.pad(UpperCamelCase_				,    truncation=UpperCamelCase_ )[input_name]

           # padding has to be max_length when setting `truncation=True`
           with self.assertRaises(UpperCamelCase_ ):
                  feat_extract.pad(UpperCamelCase_				,    padding='longest'				,    truncation=UpperCamelCase_ )[input_name]

           # padding has to be max_length when setting `truncation=True`
           with self.assertRaises(UpperCamelCase_ ):
                  feat_extract.pad(UpperCamelCase_				,    padding='longest'				,    truncation=UpperCamelCase_ )[input_name]

           # max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
           with self.assertRaises(UpperCamelCase_ ):
                  feat_extract.pad(UpperCamelCase_				,    padding='max_length'				,    truncation=UpperCamelCase_ )[input_name]

           # test truncation for `pad_to_multiple_of` for List[int] + numpy
           __magic_name__      				=    1_2
           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    max_length=len(speech_inputs[0] )				,    pad_to_multiple_of=UpperCamelCase_				,    truncation=UpperCamelCase_				,    )
           __magic_name__      				=    input_a[input_name]

           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    max_length=len(speech_inputs[0] )				,    pad_to_multiple_of=UpperCamelCase_				,    )
           __magic_name__      				=    input_a[input_name]

           # retrieve expected_length as multiple of pad_to_multiple_of
           __magic_name__      				=    len(speech_inputs[0] )
           if expected_length % pad_to_multiple_of != 0:
                  __magic_name__      				=    ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of

           self.assertTrue(len(input_a[0] ) == expected_length )
           self.assertTrue(_inputs_have_equal_length(UpperCamelCase_ ) )
           self.assertFalse(_inputs_have_equal_length(UpperCamelCase_ ) )







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           self._check_padding(numpify=UpperCamelCase_ )







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           self._check_padding(numpify=UpperCamelCase_ )







    def 				a__			(		self :	List[Any] ):





           '''simple docstring'''

           self._check_truncation(numpify=UpperCamelCase_ )







    def 				a__			(		self :	int ):





           '''simple docstring'''

           self._check_truncation(numpify=UpperCamelCase_ )







    @require_torch
    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.feature_extraction_class(**self.feat_extract_dict )
           __magic_name__      				=    self.feat_extract_tester.prepare_inputs_for_common()
           __magic_name__      				=    feat_extract.model_input_names[0]

           __magic_name__      				=    BatchFeature({input_name: speech_inputs} )

           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding='longest'				,    return_tensors='np' )[input_name]
           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding='longest'				,    return_tensors='pt' )[input_name]

           self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )







    @require_tf
    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.feature_extraction_class(**self.feat_extract_dict )
           __magic_name__      				=    self.feat_extract_tester.prepare_inputs_for_common()
           __magic_name__      				=    feat_extract.model_input_names[0]

           __magic_name__      				=    BatchFeature({input_name: speech_inputs} )

           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding='longest'				,    return_tensors='np' )[input_name]
           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding='longest'				,    return_tensors='tf' )[input_name]

           self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )







    def 				a__			(		self :	str ):





           '''simple docstring'''

           __magic_name__      				=    self.feat_extract_dict
           __magic_name__      				=    True
           __magic_name__      				=    self.feature_extraction_class(**UpperCamelCase_ )
           __magic_name__      				=    self.feat_extract_tester.prepare_inputs_for_common()
           __magic_name__      				=    [len(UpperCamelCase_ ) for x in speech_inputs]
           __magic_name__      				=    feat_extract.model_input_names[0]

           __magic_name__      				=    BatchFeature({input_name: speech_inputs} )

           __magic_name__      				=    feat_extract.pad(UpperCamelCase_				,    padding='longest'				,    return_tensors='np' )
           self.assertIn('attention_mask'				,    UpperCamelCase_ )
           self.assertListEqual(list(processed.attention_mask.shape )				,    list(processed[input_name].shape[:2] ) )
           self.assertListEqual(processed.attention_mask.sum(-1 ).tolist()				,    UpperCamelCase_ )







    def 				a__			(		self :	Optional[Any] ):





           '''simple docstring'''

           __magic_name__      				=    self.feat_extract_dict
           __magic_name__      				=    True
           __magic_name__      				=    self.feature_extraction_class(**UpperCamelCase_ )
           __magic_name__      				=    self.feat_extract_tester.prepare_inputs_for_common()
           __magic_name__      				=    [len(UpperCamelCase_ ) for x in speech_inputs]
           __magic_name__      				=    feat_extract.model_input_names[0]

           __magic_name__      				=    BatchFeature({input_name: speech_inputs} )
           __magic_name__      				=    min(UpperCamelCase_ )

           __magic_name__      				=    feat_extract.pad(
               UpperCamelCase_				,    padding='max_length'				,    max_length=UpperCamelCase_				,    truncation=UpperCamelCase_				,    return_tensors='np' )
           self.assertIn('attention_mask'				,    UpperCamelCase_ )
           self.assertListEqual(
               list(processed_pad.attention_mask.shape )				,    [processed_pad[input_name].shape[0], max_length] )
           self.assertListEqual(
               processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist()				,    [max_length for x in speech_inputs] )







"""simple docstring"""
from maths.prime_check import is_prime


def 							A						(				__snake_case: int  )    ->		int:

       """simple docstring"""





       if not isinstance(__snake_case ,     __snake_case  ):
              __magic_name__      				=    F"""Input value of [number={number}] must be an integer"""
              raise TypeError(__snake_case  )
       if is_prime(__snake_case  ) and is_prime(number + 2  ):
              return number + 2
       else:
              return -1


if __name__ == "__main__":
 import doctest

 doctest.testmod()